Khalid Kalil’s MSc thesis at Cranfield University involved the use of CFD to define the flowfield around multiple vehicles traveling along a roadway. The results can be used to train a driverless car’s artificial intelligence so it can travel not only efficiently but safely.

“The concept is to teach these [driverless] vehicles to position themselves on the road in areas with less drag for optimal fuel efficiency, travel time, stability and ultimately for maximum safety,” says Khalil.

Working with his professors and advisors, Khalil began by gathering data about road conditions on the UK’s M1 roadway such as traffic patterns, accidents, winds, and speed. He then used Pointwise to create individual high-fidelity meshes for the subject car and a variety of vehicles. After inputting the road conditions and running simulations on the individual aerodynamics of the main vehicle, he joined all the meshes together using Pointwise’s multiple block feature for the final study of how the moving parts interacted together.

This is just the beginning for researchers. The data and results from Khalil’s numerical study will be used by future researchers to refine the automotive industry’s understanding of how changing aerodynamic conditions affect vehicles on the road.